Towards a unified classification for human respiratory syncytial virus genotypes
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Virus Evolution, 2020, 6(2): veaa052
doi: 10.1093/ve/veaa052
Research Article
Towards a unified classification for human respiratory
syncytial virus genotypes
Kaat Ramaekers,1,*,† Annabel Rector,1,‡ Lize Cuypers,1,2,§ Philippe Lemey,1,**
Els Keyaerts,1,2,†† and Marc Van Ranst1,2,‡‡
1
KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical
Research, Laboratory of Clinical and Epidemiological Virology, Herestraat 49 box 1040, BE-3000 Leuven,
Belgium and 2University Hospitals Leuven, Department of Laboratory Medicine and National Reference
Centre for Respiratory Pathogens, Herestraat 49, BE-3000 Leuven, Belgium
*Corresponding author: E-mail: kaat.ramaekers@kuleuven.be
†
https://orcid.org/0000-0002-7958-4891
‡
https://orcid.org/0000-0003-3714-449X
§
https://orcid.org/0000-0002-9433-8752
**https://orcid.org/0000-0003-2826-5353
††
https://orcid.org/0000-0002-1849-3240
‡‡
https://orcid.org/0000-0002-1674-4157
Abstract
Since the first human respiratory syncytial virus (HRSV) genotype classification in 1998, inconsistent conclusions have been
drawn regarding the criteria that define HRSV genotypes and their nomenclature, challenging data comparisons between
research groups. In this study, we aim to unify the field of HRSV genotype classification by reviewing the different methods
that have been used in the past to define HRSV genotypes and by proposing a new classification procedure, based on well-
established phylogenetic methods. All available complete HRSV genomes (>12,000 bp) were downloaded from GenBank and
divided into the two subgroups: HRSV-A and HRSV-B. From whole-genome alignments, the regions that correspond to the
open reading frame of the glycoprotein G and the second hypervariable region (HVR2) of the ectodomain were extracted. In
the resulting partial alignments, the phylogenetic signal within each fragment was assessed. Maximum likelihood phyloge-
netic trees were reconstructed using the complete genome alignments. Patristic distances were calculated between all pairs
of tips in the phylogenetic tree and summarized as a density plot in order to determine a cutoff value at the lowest point fol-
lowing the major distance peak. Our data show that neither the HVR2 fragment nor the G gene contains sufficient phyloge-
netic signal to perform reliable phylogenetic reconstruction. Therefore, whole-genome alignments were used to determine
HRSV genotypes. We define a genotype using the following criteria: a bootstrap support of 70 per cent for the respective
clade and a maximum patristic distance between all members of the clade of 0.018 substitutions per site for HRSV-A or
0.026 substitutions per site for HRSV-B. By applying this definition, we distinguish twenty-three genotypes within subtype
HRSV-A and six genotypes within subtype HRSV-B. Applying the genotype criteria on subsampled data sets confirmed the
robustness of the method.
Key words: human respiratory syncytial virus; classification; genotypes.
C The Author(s) 2020. Published by Oxford University Press.
V
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12 | Virus Evolution, 2020, Vol. 6, No. 2
1. Introduction suggesting a selective advantage (Trento et al. 2006; Eshaghi
et al. 2012; Pangesti et al. 2018). Because of its high variability,
Human respiratory syncytial virus (HRSV) is worldwide the
evaluation of HRSV genetic diversity has historically relied most
most common viral cause of acute respiratory tract infections in
often on the G gene (Cane et al. 1991; Garcia et al. 1994).
children up to the age of 5 years (Shi et al. 2017). Currently, there
Since the first HRSV genotype classification by Peret et al.
is no licensed vaccine available, and treatment options are
(1998), several research groups have shown their interest in
scarce (Heylen et al. 2017). Mechanisms to evade the host im-
HRSV genetic diversification, resulting in the discovery of addi-
mune responses and the young age of the main target patient
tional genotypes. Inconsistent conclusions have been drawn re-
group are only two examples of the challenges related to the de-
garding the number of genotypes, based on different parts of
velopment of HRSV antiviral therapies and vaccines (Simoes
the HRSV genome. Moreover, different criteria have been used
et al. 2015; Battles and McLellan 2019; Rossey and Saelens 2019).
to define genotypes, whereas similarly defined genotypes have
Furthermore, HRSV diversity is more shaped by temporal than
been named inconsistently: either based on the subtype and
by geographical distribution with rapid global spread of new
gene that was studied, based on the country or the city where
variants (Pangesti et al. 2018). In temperate climates, HRSV cir-
the genotypes were first described, and based on their phyloge-
culates according to a reoccurring seasonal pattern with peaks
netic clustering or sometimes even seemingly arbitrary.
during the late fall or early winter, whereas the peak in tropical
climates occurs in the late summer months (Ramaekers et al.
2017; Li et al. 2019). To support ongoing developments of new
1.1 Genotype classification based on the HVR2 region of
therapies, mapping the genetic diversity of HRSV remains of the G gene
critical importance. The first classification system of HRSV genotypes, proposed in
HRSV has since 2018 been reclassified under the species 1998, relied on sequencing information of the second HVR2 of
name Human orthopneumovirus within the family of the G gene. Based on visual inspection of a phylogenetic tree,
Pneumoviridae (Rima et al. 2017). The genome of HRSV is single- seven genotypes could be distinguished for HRSV-A and four
stranded negative sense RNA with a length of 15.2 kb and is genotypes for HRSV-B. Bootstrap support (BS) values of 78 per
composed of ten genes which code for eleven proteins: three cent or higher were observed for the relevant clusters. The gen-
transmembrane glycoproteins (F, G, SH), two matrix proteins otypes were named based on the gene used for classification
(M, M2), three proteins associated with the nucleocapsid (N, P, (G), followed by the HRSV subtype (A or B) and an ascending
L), and two nonstructural proteins (NS1, NS2) (Battles and number: GA1–GA7 and GB1–GB4 (Peret et al. 1998, 2000)
McLellan 2019) (Fig. 1). The attachment protein (G) and the fu- (Table 1).
sion protein (F) are responsible for the attachment and entry of One year later, Venter et al. used a similar approach to ex-
the viral particle into the host cell and are therefore important pand the number of HRSV genotypes. The method of classifica-
targets of the host immune responses (Collins and Melero 2011). tion described by Peret et al. was refined by including genetic
Two subtypes of HRSV (A and B) have been distinguished by distance as a metric to define clusters. If a group of sequences
using monoclonal antibodies against the G, F, M, NP, and P pro- would cluster together with BS values of 70 per cent or more
teins (Anderson et al. 1985; Mufson et al. 1985; Gimenez et al. and if characterized with a pairwise distance ofK. Ramaekers et al. | 3
Table 1. Overview of criteria and nomenclature used in literature to define HRSV genotypes.
Reference Genotyping region Genotyping Genotype Genotypes Genotypes
definitions nomenclature identified HRSV-A identified HRSV-B
Peret et al. (1998) HVR2 BS 78% Gene þ subtype þ GA1–GA5 GB1–GB4
number
Peret et al. (2000) HVR2 BS 70% Gene þ subtype þ GA6–GA7 /
number
Choi and Lee (2000) G gene (HRSV-A) or Restriction analysis Gene þ P þ subtype GP-A1–GP-A24 GP-B1–GP-B6
partial HVR2 G gene þ number
(HRSV-B)
Venter et al. (2001) HVR2 BS 70%, p distance Country þ subtype þ SAA1 SAB1–SAB3
77% Country þ subtype þ BE/A1 /
number
Zlateva et al. (2005) Ectodomain BS > 77% Gene þ subtype þ / GB5–GB13
number
Blanc et al. (2005) HVR2 BS 70%, p distance Country þ subtype þ / URU1–URU2
50% Clustering þ number / BA7–BA10
Arnott et al. (2011) HVR2 BS 70%, p distance Clustering þ number / SAB44 | Virus Evolution, 2020, Vol. 6, No. 2
used for the BA genotype and its subclades (Liu et al. 2014) WHO, in which clade names would be derived from the names
(Table 1). of the genotype they originate from. Additionally, clades would
be defined based on BS values of 60 per cent or higher and with
1.2 Genotype classification based on the ectodomain of an average genetic distance of at least 1.5 per cent to other
the G gene clades andK. Ramaekers et al. | 5
With this manuscript, we aim to unify the field by reviewing 2.4 Defining HRSV genotypes using patristic distance
the different methods that have been used in the past to define and BS
HRSV genotypes and by proposing a new classification proce-
Patristic distances, that is the shortest distance between two
dure, based on well-established phylogenetic methods. This
tips, measured as the sum of the branch lengths, were calcu-
project is part of a joined effort embedded within the GeNom lated between all tips of the phylogenetic trees based on the
consortium which aims to improve the global surveillance of alignment of the whole-genome lacking the duplicated region,
HRSV by creating well-defined guidelines for the genotyping using the adephylo v1.1 package in R (Jombart and Dray 2010). A
and nomenclature of HRSV strains. density plot of the resulting patristic distances was created us-
ing the ggplot2 package in R (Wickham 2016) and used to visu-
2. Methodology ally determine a cutoff value at the lowest point following the
major distance peak (Prosperi et al. 2011), respectively, for
2.1 Compilation of a full-length HRSV genome data set HRSV-A and HRSV-B.
All available complete HRSV genomes (>12,000 bp) were down- Two criteria were used to distinguish genotypes in each sub-
loaded from GenBank on January 15, 2019. The resulting 2,212 type of HRSV: 1. the maximum patristic distance (PatDist_max)
sequences were first confirmed as HRSV genomes, using CD- between all tips within the clade is below the cutoff value of the
whole tree and 2. the bootstrap value of the parent node of the
HIT v4.5.4 at a sequence identity threshold of 80 per cent (Li and
clade is 70 per cent or higher. The R packages ape v 5.3 (Paradis
Godzik 2006). The two largest clusters were retained and clus-
and Schliep 2019) and adephylo v1.1 (Jombart and Dray 2010)
tered into HRSV-A and HRSV-B, using the default 90 per cent se-
were used to define genotypes, whereas visualizations were cre-
quence identity threshold of CD-HIT v4.5.4. Upon removal of
ated with ggtree v1.16.0 (Yu et al. 2017, 2018), and ggplot2 v3.1.1
inadequate strains for phylogenetic analysis (i.e. mutants, pat-
(Wickham 2016).
ented sequences, duplicates, cell culture strains, and partial
genomes with gaps of 100 bp or more), alignments were gener-
ated for each subtype, using the default options of MAFFT v7
2.5 Method robustness
(Katoh and Standley 2013). Alignments were visually inspected Finally, to correct for the overrepresentation of recently sam-
and edited in AliView v1.23 (Larsson 2014). pled strains, the analysis was repeated with data sets in which
The resulting alignments were screened for sequences with the overrepresented group was reduced. A subset of ten sequen-
potential recombination events, using the detection methods ces, representing the large majority of the genetic diversity em-
RDP, GENECONV, MaxChi, BootScan, and SiScan as imple- bedded within the overrepresented cluster, was determined for
mented in the Recombination Detection Program RDP4 (Martin both data sets (HRSV-A and HRSV-B), using the software pack-
et al. 2015). After additional exclusion of all strains flagged as age Phylogenetic Diversity Analyzer (PDA v1.0.3) (Chernomor
potential recombinants, final alignments of, respectively, 861 et al. 2015). Phylogenetic tree reconstruction and genotype de-
sequences for subgroup HRSV-A and 492 sequences for sub- marcation were repeated on the reduced data sets, comprising
group HRSV-B were used for further analysis. of 261 and 70 taxa, respectively, as described earlier.
2.2 Evaluation of phylogenetic signal 3. Results
Starting from whole-genome alignments, we extracted the 3.1 Artificial recombination in both subtypes
regions that correspond to the open reading frame (ORF) of the
Although recombination within the HRSV genome has not been
Glycoprotein G and the second HVR2 of the ectodomain, either
described in natural strains (Tan et al. 2012), we tested our data
with or without inclusion of the duplication within the HVR2 re-
set for the presence of possible recombination events prior to
gion. For the resulting partial alignments, the phylogenetic sig-
proceeding with phylogenetic analyses. In both HRSV subtypes,
nal within each fragment was assessed, using the likelihood
potential recombination events were flagged in the data set.
mapping function as implemented in Tree-Puzzle v5.3 (Schmidt
These events are most likely artificial recombination events as
et al. 2002).
a result of errors during the assembly of shorter sequencing
fragments. Especially when whole-genome sequences have
2.3 Phylogenetic tree reconstruction been obtained by the use of metagenomics, caution is needed to
The best-fitting nt substitution model for each data set was avoid mistakes during de novo assembly (Tan et al. 2012;
identified by comparing eighty-eight candidate models using Simmonds et al. 2017).
jModeltest v2.1.10 (Guindon and Gascuel 2003; Darriba et al.
2012). Phylogenetic trees of the complete genome alignments 3.2 The G gene and HVR2 region are not suitable for
were reconstructed using RAxML v8.2.12, using the Generalized phylogenetic analysis
Time Reversible substitution model with a gamma model of Quality assessment of 2,212 genome sequences from GenBank
rate heterogeneity and taking invariable sites into account (GTR resulted in final data sets consisting of 861 sequences for HRSV-
þ GAMMA þ I) (Stamatakis 2014). Branch support was evaluated A and 493 for HRSV-B. Genome fragments of the G gene ORF
by bootstrapping based on 1,000 pseudoreplicates. Nt sequences (967 nt/951 nt) and the HVR2 region (409 nt/399 nt) were
of both HRSV subtypes (HRSV-A vs. HRSV-B) do not align unam- extracted from the whole-genome nt alignments (14,953 nt/
biguously and are therefore not closely enough related to be 14,949 nt) for subtype HRSV-A and HRSV-B, respectively. The
used as a meaningful outgroup for each other (Salemi 2009). phylogenetic signal of each fragment, with and without the du-
Therefore, trees were midpoint rooted using the R package phy- plicated region present, was assessed, using the likelihood-
tools v0.6 (Revell 2012), and rendered with increasing node order mapping algorithm implemented in Tree-Puzzle
using the R package ape v5.3 (Paradis and Schliep 2019). (Supplementary Fig. S1). To allow reliable reconstruction of a6 | Virus Evolution, 2020, Vol. 6, No. 2
Table 2. Percentages of resolved phylogenies in different fragments of the HRSV genome.
Alignment Unresolved (%) Conflict (%) Resolved (%)
HRSV-A HRSV-B HRSV-A HRSV-B HRSV-A HRSV-B
HVR2 region w/o duplication 16.2 12.7 2.6 4.2 81.2 83.1
HVR2 region w/ duplication 16.4 12.5 2.4 4 81.2 83.5
G gene w/o duplication 12 6.6 3 3.2 85.0 90.2
G gene w/ duplication 11.5 7.7 3 3.3 85.5 89.0
Full genome w/o duplication 1.1 0.7 1.9 1.4 97.0 97.9
Full genome w/ duplication 1.3 0.6 1.8 1.1 96.9 98.3
With exception of the HRSV-B G gene without duplication, for both subtypes only the whole-genome alignments show sufficient phylogenetic signal (defined as re-
solved phylogenies for at least 90% of the quartets, in bold) for reliable phylogenetic analysis (Supplementary Fig. S1).
phylogenetic tree, a fragment should have at least 90 per cent 4. Discussion
phylogenetic support, determined as the percentage of resolved
Human respiratory syncytial virus is one of the most important
phylogenies in Tree-Puzzle (Schmidt et al. 2002). The align-
causes of acute respiratory tract infections and is estimated to
ments of the shorter fragments (G gene and HVR2) do not meet
cause 76,600 annual deaths, primarily in young children under
this criterion and are therefore not suitable to proceed to phylo-
the age of five and in the elderly population (Li et al. 2019). We
genetic analysis, with exception of the HRSV-B G gene align-
are now on the verge of having a licensed HRSV vaccine on the
ment without the duplicated region. The whole-genome
market and several antivirals are under development (Higgins
alignments were supported well beyond the 90 per cent cutoff et al. 2016; Heylen et al. 2017; PATH 2019; Rossey and Saelens
for both subtypes, regardless of inclusion or exclusion of the du- 2019). A systematic monitoring of genetic diversity of the circu-
plicated nt stretch in the HVR2 region (Table 2). lating strains is essential for a good understanding of the long-
term effectiveness of vaccines (Otieno et al. 2016). However, due
to lack of consistency in the methodology and nomenclature of
3.3 Patristic distances and BS values define HRSV
HRSV classification, several genotype definitions are being used
genotypes
in parallel, creating confusion and leading to difficulties in com-
ML trees were reconstructed for both subtypes based on whole- paring data from different areas in the world. Furthermore, the
genome alignments excluding the duplicated region. The patris- correlation between disease severity and genotype has been un-
tic distances for all pairs of taxa were calculated in each phylog- der discussion for several years due to conflicting results
eny and summarized as a density plot. Cutoff values were (Anderson et al. 2019; Vos et al. 2019). All these reasons call for a
determined objectively by identifying the lowest point following unified classification system (Cane 2001; Agoti et al. 2014;
the major distance peak, which resulted in distances of 0.018 nt Trento et al. 2015; Pangesti et al. 2018).
substitutions per site for HRSV-A and 0.026 subst./site for HRSV- In recent years, several research groups have attempted to
B (Fig. 2). update the classification methodology, but so far no one suc-
Genotypes within each subtype were defined based on the ceeded to encourage fellow researchers to follow their approach
patristic distance cutoff value and a BS of at least 70 per cent. (Agoti et al. 2014, 2015b, 2017; Liu et al. 2014; Trento et al. 2015).
For HRSV-A, twenty-three clades (A1–A23) were defined based Based on the highest intragenotypic p-distance as the minimal
on our genotype criteria (BS 70%, PatDist_max 0.018 subst./ threshold to define a genotype, a suggestion has been proposed
site), whereas six genotypes (B1–B6) were distinguished for to reduce the number of genotypes within subtype HRSV-A
from fourteen to seven (Trento et al. 2015). The proposal failed
HRSV-B (BS 70%, PatDist_max 0.026 subst./site) (Fig. 3). The
to become the new reference in the HRSV field possibly due to
accession numbers of all members per genotype are listed in
its restriction to only one out of two HRSV subtypes. The sug-
Supplementary Table S1.
gestion to use an influenza-like system for genotype and sub-
clade classification meets this limitation but has not been
3.4 Robust method for application on smaller data sets adopted beyond the proposing research group either (Agoti
et al. 2014). In addition to suggesting a well-founded and robust
By applying these genotype criteria to the phylogenetic tree
classification method, receiving support from several authori-
reconstructed for a subsampled HRSV-A alignment, in which
ties in the field proves to be essential in order to make a change
the presence of recent strains was downsized, we could dis-
in the current practice. Therefore, the GeNom consortium
criminate twenty-two instead of twenty-three HRSV-A geno-
attempts to reach harmony within HRSV strain nomenclature
types. Taxa that cluster together in the phylogeny of the full and genotype classification by combining the efforts of several
data set (n ¼ 861) also cluster together in the phylogeny of the authorities in the field. In this study, we thoroughly reviewed
subset (n ¼ 261), with the exception of taxa of genotypes A22 all methods currently used with respect to the classification of
and A23 in the full data set analysis that are now combined into HRSV genotypes. Over the last two decades, we could appreciate
one group (Fig. 4). For subtype HRSV-B, the number of genotypes a tremendous improvement in laboratory techniques and soft-
was identical, that is, six, whether the genotype criteria were ware development, which makes comparison of existing meth-
applied on the full data set alignment (n ¼ 493) or on the sub- ods challenging. Therefore, instead of making a choice between
sampled alignment (n ¼ 70), with 100 per cent of the taxa clus- existing methods, we decided to use a bottom-up approach by
tering together in the same way (data not shown). starting from the available sequence data in GenBank. WithK. Ramaekers et al. | 7
A B
60
40
Density (%)
20
0
0.000 0.025 0.050 0.075
0.004 Patristic distance (subst./site)
C D
60
40
Density (%)
20
0
0.00 0.01 0.02 0.03 0.04 0.05
0.002
Patristic distance (subst./site)
Figure 2. Whole-genome phylogenies and density distributions of patristic distances for HRSV-A and HRSV-B. Patristic distances were calculated between all tips of the
ML trees of whole-genome alignments of HRSV-A (A) and HRSV-B (C) and cut-off values were chosen at the lowest point after the major peak in the density plot, deter-
mined at 0.018 subst./site for HRSV-A (B) and slightly higher at 0.026 subst./site for HRSV-B (D).
this approach, we aim to propose a classification system The most commonly used definition to classify HRSV strains
based on patterns in the data, rather than historical preferences into genotypes is still the original definition, formulated by
for a prevailing research track, tradition or practical Peret et al. in 1998 and refined by Venter et al. in 2001, in which
considerations. a genotype is distinguished based on a phylogenetic cluster8 | Virus Evolution, 2020, Vol. 6, No. 2
A B
A23
B6
A22
A21
A20
A19 A18
A17
A16 A15
A14
A13
A12
A11
A10
A9 B5
A8 B4
A7
A6
A5 B3
A4 A3
A2 B2
A1 B1
0.004 0.002
Figure 3. Genotypes defined within subtypes HRSV-A and HRSV-B. Based on the genotype criteria of BS 70 per cent, PatDist_max 0.018 subst./site (HRSV-A), or
0.026 subst./site (HRSV-B), we distinguish twenty-three and six genotypes for subtype HRSV-A (A) and HRSV-B (B), respectively. Underneath each tree, the evolution-
ary distance scale is indicated, expressed as nt subst./site.
process and was therefore applied to phylogenies of the HVR2
23
22 region of the G gene, which is a highly variable region of the ge-
21
20
nome and thus expected to be informative for phylogenetic
19 analysis and classification. Technological advances have made
Genotypes complete dataset
18
17 nt sequencing of larger fragments, and even complete genomes,
16 value
15 100 easier and cheaper, eliminating the need to use short fragments
14 for genotype classification (Thomson et al. 2016; Goya et al.
13 75
12
50
2018). Additionally, studies in mice have indicated the impor-
11
10 tance of the F protein and of the central conserved domain of
25
9
8
the G protein in the development and severity of the associated
0
7 disease (Tripp et al. 2001; Hotard et al. 2015; Currier et al. 2016;
6
5 Boyoglu-Barnum et al. 2017). As a consequence of the persistent
4
3 focus on the HVR2 region of the G gene for genotyping, an asso-
2
1
ciation between virulence and viral genotype may have been
0 missed (Anderson et al. 2019). In order to assess the loss of in-
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Genotypes subset
formation when using the HVR2 region of the G gene, we evalu-
ated the phylogenetic signal in this fragment, as well as in the G
Figure 4. Genotype co-occurrence matrix HRSV-A. The heat map shows for each
gene in comparison to the whole-genome alignment. Although
genotype defined using the full data set (y axis), the percentage of taxa that cor-
the HVR2 region has been proposed as a good proxy to assess
responds to the genotypes defined using a subset of the data set (x axis). The
correlation of the taxa is 100 per cent for all genotypes, with the exception of the the variability for the whole genome (Peret et al. 1998), our
taxa within genotypes A22 and A23 that are merged into one genotype when us- analysis shows that neither the HVR2 fragment nor the G gene
ing a subset of the data. contains sufficient phylogenetic signal to perform reliable phy-
logenetic reconstruction. Whole-genome data from all over the
with BS of at least 70 per cent and a pairwise distance ofK. Ramaekers et al. | 9
due to a shortage of whole-genome strains for the subtype Respiratory Syncytial Virus (RSV) in a Long-Term Refugee
HRSV-B (Hadfield et al. 2018). Further motivated by the large in- Camp in Kenya’, BMC Infectious Diseases, 14: 178.
terest of antiviral (Heylen et al. 2017) and vaccine (Rossey and et al. (2017) ‘Transmission Patterns and Evolution of
Saelens 2019) research in parts of the HRSV genome other than Respiratory Syncytial Virus in a Community Outbreak
the G gene, we suggest using whole-genome phylogenies as the Identified by Genomic Analysis’, Virus Evolution, 3: vex006.
basis for a future genotype classification system. et al. (2015a) ‘Local Evolutionary Patterns of Human
In our method, we chose, in addition to BS values, patristic Respiratory Syncytial Virus Derived from Whole-Genome
distances as a parameter to distinguish genotypes rather than Sequencing’, Journal of Virology, 89: 3444–54.
pairwise distances. Patristic distance is a tree-based estimation et al. (2015b) ‘Successive Respiratory Syncytial Virus
of the genetic distance, measured as the shortest distance over Epidemics in Local Populations Arise from Multiple Variant
the branch lengths between two tips of the phylogenetic tree. Introductions, Providing Insights into Virus Persistence’,
Therefore, patristic distances reflect the information from the Journal of Virology, 89: 11630–42.
evolutionary model that was chosen to build the phylogenetic Anderson, L. J. et al. (1985) ‘Antigenic Characterization of
tree and result in a better estimation of the true genetic distan- Respiratory Syncytial Virus Strains with Monoclonal
ces represented in the data set compared with pairwise distan- Antibodies’, The Journal of Infectious Diseases, 151: 626–33.
ces (Lemey et al. 2009). Patristic distances were calculated et al. (2019) ‘RSV Strains and Disease Severity’, The Journal
between all pairs of tips of the phylogenetic trees of the whole- of Infectious Diseases, 219: 514–6.
genome alignments of HRSV-A and HRSV-B. The resulting ma- Arnott, A. et al. (2011) ‘A Study of the Genetic Variability of
trix was visualized in a density plot and the cutoff to distinguish Human Respiratory Syncytial Virus (HRSV) in Cambodio
a genotype was chosen at the lowest point after the major peak Reveals the Existence of a New HRSV Group B Genotype’,
(Prosperi et al. 2011). Journal of Clinical Microbiology, 49: 3504–53.
We define a clade as a genotype when the following criteria Baek, H. Y. et al. (2012) ‘Prevalence and Genetic Characterization
are met: the respective clade has a BS of 70 per cent and the
of Respiratory Syncytial Virus (RSV) in Hospitalized Children
maximum patristic distance between all members of the clade
in Korea’, Archives of Virology, 157: 1039–50.
is 0.018 subst./site for HRSV-A or 0.026 subst./site for HRSV-
Bartholomeusz, A., and Schaefer, S. (2004) ‘Hepatitis B Virus
B. By applying this definition, we distinguish twenty-three gen-
Genotypes: Comparison of Genotyping Methods’, Reviews in
otypes within subtype HRSV-A (A1–A23) and six genotypes
Medical Virology, 14: 3–16.
within subtype HRSV-B (B1–B6). The genotypes were temporar-
Battles, M. B., and McLellan, J. S. (2019) ‘Respiratory Syncytial
ily named by ascending numbers until a suitable nomenclature
Virus Entry and How to Block It’, Nature Reviews Microbiology,
can be defined. In order to test the robustness of our proposed
17: 233–45.
genotype definition, we redefined the genotypes on subsets of
Blanc, A. et al. (2005) ‘Genotypes of Respiratory Syncytial Virus
the initial HRSV-A and HRSV-B data sets. Twenty-two out of
Group B Identified in Uruguay’, Archives of Virology Virol, 150:
twenty-three HRSV-A genotypes and all six HRSV-B genotypes
603–9.
were distinguished, with the same taxa clustering together,
Boyoglu-Barnum, S. et al. (2017) ‘Mutating the CX3C Motif in the
confirming the robustness of our method to a high extent.
G Protein Should Make a Live Respiratory Syncytial Virus
The evolution of HRSV strains is a continuous process, with
Vaccine Safer and More Effective’, Journal of Virology, 91:
relatively rapid sequential replacement of dominating strains
e02059–16.
about every 7 years (Otieno et al. 2016). Consequently, HRSV
Cane, P. A. (2001) ‘Molecular Epidemiology of Respiratory
classification, including the cutoffs used, may need further
Syncytial Virus’, Reviews in Medical Virology, 11: 103–16.
updating in the future with the prospect of increasing popula-
et al. (1991) ‘Identification of Variable Domains of the
tion turnover and sequence sampling. With our approach based
on data patterns in complete genomes, we aim to formulate a Attachment (G) Protein of Subgroup A Respiratory Syncytial
strategy for future HRSV genotype classification. Viruses’, Journal of General Virology, 72: 2091–6.
et al. (1992) ‘Analysis of Relatedness of Subgroup A
Respiratory Syncytial Viruses Isolated Worldwide’, Virus
Funding Research, 25: 15–22.
The National Reference Center for Respiratory Pathogens re- , and Pringle, C. R. (1991) ‘Respiratory Syncytial Virus
ceived funding from Sciensano, the Institute of Public Heterogeneity during an Epidemic: Analysis by Limited
Health. Nucleotide Sequencing (SH Gene) and Restriction Mapping (N
Gene)’, The Journal of General Virology, 72: 349–57.
Chernomor, O. et al. (2015) ‘Split Diversity in Constrained
Data availability Conservation Prioritization Using Integer Linear
Data are publicly available on GenBank. Programming’, Methods in Ecology and Evolution, 6: 83–91.
Choi, E. H., and Lee, H. J. (2000) ‘Genetic Diversity and Molecular
Epidemiology of the G Protein of Subgroups A and B of
Supplementary data Respiratory Syncytial Viruses Isolated over 9 Consecutive
Supplementary data are available at Virus Evolution online. Epidemics in Korea’, The Journal of Infectious Diseases, 181:
1547–56.
Conflict of interest: None declared. Collins, P. L., and Melero, J. A. (2011) ‘Progress in Understanding
and Controlling Respiratory Syncytial Virus: Still Crazy after
All These Years’, Virus Research, 162: 80–99.
References Cui, G. et al. (2013a) ‘Emerging Human Respiratory Syncytial
Agoti, C. N. et al. (2014) ‘Examining Strain Diversity and Virus Genotype ON1 Found in Infants with Pneumonia in
Phylogeography in Relation to an Unusual Epidemic Pattern of Beijing’, Emerging Microbes & Infections, 2: 1–2.10 | Virus Evolution, 2020, Vol. 6, No. 2
et al. (2013b) ‘Genetic Variation in Attachment Larsson, A. (2014) ‘AliView: A Fast and Lightweight Alignment
Glycoprotein Genes of Human Respiratory Syncytial Virus Viewer and Editor for Large Datasets’, Bioinformatics, 30: 3276–8.
Subgroups A and B in Children in Recent Five Consecutive Lemey, P., Vandamme, A.-M., and Salemi, M. (2009) The
Years’, PLoS One, 8: e75020. Phylogenetic Handbook. A Practical Approach to Phylogenetic
Currier, M. G. et al. (2016) ‘EGFR Interacts with the Fusion Protein Analysis and Hypothesis Testing, second edition. Cambridge
of Respiratory Syncytial Virus Strain 2-20 and Mediates University Press, Cambridge, UK.
Infection and Mucin Expression’, PLoS Pathogens, 12: e1005622. Li, W., and Godzik, A. (2006) ‘Cd-Hit: A Fast Program for
Cuypers, L. et al. (2018) ‘Time to Harmonize Dengue Clustering and Comparing Large Sets of Protein or Nucleotide
Nomenclature and Classification’, Viruses, 10: 569. Sequences’, Bioinformatics (Oxford, England)), 22: 1658–9.
Dapat, I. C. et al. (2010) ‘New Genotypes within Respiratory Li, Y. et al. (2019) ‘Global Patterns in Monthly Activity of
Syncytial Virus Group B Genotype BA in Niigata’, Journal of Influenza Virus, Respiratory Syncytial Virus, Parainfluenza
Clinical Microbiology, 48: 3423–7. Virus, and Metapneumovirus: A Systematic Analysis’, The
Darriba, D. et al. (2012) ‘JModelTest 2: More Models, New Lancet Global Health, 7: e1031.4.
Heuristics and Parallel Computing’, Nature Methods, 9: 772–66. Liu, J. et al. (2014) ‘Genetic Variation of Human Respiratory
Di Giallonardo, F. et al. (2018) ‘Evolution of Human Respiratory Syncytial Virus among Children with Fever and Respiratory
Syncytial Virus (RSV) over Multiple Seasons in New South Symptoms in Shanghai, China, from 2009 to 2012’, Infection,
Wales, Australia’, Viruses, 10: 476–13. Genetics and Evolution, 27: 131–6.
Eshaghi, A. et al. (2012) ‘Genetic Variability of Human Martin, D. P. et al. (2015) ‘RDP4: Detection and Analysis of
Respiratory Syncytial Virus a Strains Circulating in Ontario: A Recombination Patterns in Virus Genomes’, Virus Evolution, 1:
Novel Genotype with a 72 Nucleotide G Gene Duplication’, PLoS 1–5.
One, 7: e32807. Mufson, M. A. et al. (1985) ‘Two Distinct Subtypes of Human
Fu, L. et al. (2012) ‘CD-HIT: Accelerated for Clustering the Respiratory Syncytial Virus’, The Journal of General Virology, 66:
Next-Generation Sequencing Data’, Bioinformatics (Oxford, 2111–24.
England)), 28: 3150–2. Otieno, J. R. et al. (2016) ‘Molecular Evolutionary Dynamics of
Garcia, O. et al. (1994) ‘Evolutionary Pattern of Human Respiratory Syncytial Virus Group A in Recurrent Epidemics in
Respiratory Syncytial Virus (Subgroup A): Cocirculating Coastal Kenya’, Journal of Virology, 90: 4990–5002.
Lineages and Correlation of Genetic and Antigenic Changes in Pangesti, K. N. A. et al. (2018) ‘Molecular Epidemiology of
the G Glycoprotein’, Journal of Virology, 68: 5448–59. Respiratory Syncytial Virus’, Reviews in Medical Virology, 28:
Gimenez, H. B. et al. (1986) ‘Antigenic Variation between Human e1968.
Respiratory Syncytial Virus Isolates’, Journal of General Virology , Paradis, E., and Schliep, K. (2019) ‘Phylogenetics Ape 5.0: An
67: 863–70. Environment for Modern Phylogenetics and Evolutionary
Gimferrer, L. et al. (2016) ‘Circulation of a Novel Human Analyses in R’, Bioinformatics, 35: 526–8.
Respiratory Syncytial Virus Group B Genotype during the PATH. (2019) RSV Vaccines and MAb Snapshot. accessed 1 Dec 2019.
Goya, S. et al. (2018) ‘An Optimized Methodology for Whole Peret, T. C. T. et al. (2000) ‘Circulation Patterns of Group A and B
Genome Sequencing of RNA Respiratory Viruses from Human Respiratory Syncytial Virus Genotypes in 5
Nasopharyngeal Aspirates’, PLoS One, 13: e0199714. Communities in North America’, The Journal of Infectious
Guindon, S., and Gascuel, O. (2003) ‘A Simple, Fast, and Accurate Diseases, 181: 1891–6.
Algorithm to Estimate Large Phylogenies by Maximum et al. (1998) ‘Circulation Patterns of Genetically Distinct
Likelihood’, Systematic Biology, 52: 696–704. Group A and B Strains of Human Respiratory Syncytial Virus in
Hadfield, J. et al. (2018) ‘NextStrain: Real-Time Tracking of a Community’, The Journal of General Virology, 79: 2221–9.
Pathogen Evolution’, Bioinformatics, 34: 4121–3. Prosperi, M. C. F. et al. (2011) ‘A Novel Methodology for
Heylen, E. et al. (2017) ‘Drug Candidates and Model Systems in Large-Scale Phylogeny Partition’, Nature Communications, 2:
Respiratory Syncytial Virus Antiviral Drug Discovery’, 321.
Biochemical Pharmacology, 127: 1–12. Ramaekers, K. et al. (2017) ‘Prevalence and Seasonality of Six
Higgins, D. et al. (2016) ‘Advances in RSV Vaccine Research and Respiratory Viruses during Five Consecutive Epidemic Seasons
Development: A Global Agenda’, Vaccine, 34: 2870–75. in Belgium’, Journal of Clinical Virology, 94: 72–8.
Hotard, A. L. et al. (2015) ‘Identification of Residues in the Human Revell, L. J. (2012) ‘Phytools: An R Package for Phylogenetic
Respiratory Syncytial Virus Fusion Protein That Modulate Comparative Biology (and Other Things)’, Methods in Ecology
Fusion Activity and Pathogenesis’, Journal of Virology, 89: and Evolution, 3: 217–23.
512–22. Rima, B., et al. (2017) ‘ICTV Virus Taxonomy Profile:
Houspie, L. et al. (2013) ‘Circulation of HRSV in Belgium: From Pneumoviridae’, The Journal of General Virology, 98: 2912–3.
Multiple Genotype Circulation to Prolonged Circulation of Rossey, I., and Saelens, X. (2019) ‘Vaccines against Human
Predominant Genotypes’, PLoS One, 8: e60416. Respiratory Syncytial Virus in Clinical Trials, Where Are We
Jombart, T., and Dray, S. (2010) ‘Adephylo: Exploratory Analyses for Now?’ Expert Review of Vaccines, 18: 1053–67.
the Phylogenetic Comparative Method’, Bioinformatics, 26: 1–21. Salemi, M. (2009) ‘Phylogenetic Inference Based on Distance
Katoh, K., and Standley, D. M. (2013) ‘MAFFT Multiple Methods: Practice’, in P., Lemey, A.-M., Vandamme, and M.,
Sequence Alignment Software Version 7: Improvements in Salemi (eds) The Phylogenetic Handbook. A Practical Approach to
Performance and Usability’, Molecular Biology and Evolution, 30: Phylogenetic Analysis and Hypothesis Testing, second edition, pp.
772–80. 142–80. Cambridge University Press, Cambridge, UK.
Khor, C. S. et al. (2013) ‘Displacement of Predominant Schmidt, H. A. et al. (2002) ‘TREE-PUZZLE: Maximum Likelihood
Respiratory Syncytial Virus Genotypes in Malaysia between Phylogenetic Analysis Using Quartets and Parallel Computing’,
1989 and 2011’, Infection, Genetics and Evolution, 14: 357–60. Bioinformatics, 18: 502–4.K. Ramaekers et al. | 11
Shi, T. et al. (2017) ‘Global, Regional, and National Disease Attachment (G) Glycoprotein with a 60-Nucleotide
Burden Estimates of Acute Lower Respiratory Infections due to Duplication’, Journal of Virology, 80: 975–84.
Respiratory Syncytial Virus in Young Children in 2015: A Tripp, R. A. et al. (2001) ‘CX3C Chemokine Mimicry by
Systematic Review and Modelling Study’, The Lancet, 390: Respiratory Syncytial Virus G Glycoprotein’, Nature
946–58. Immunology, 2: 732–8.
Shobugawa, Y. et al. (2009) ‘Emerging Genotypes of Human Venter, M. et al. (2001) ‘Genetic Diversity and Molecular
Respiratory Syncytial Virus Subgroup A among Patients in Epidemiology of Respiratory Syncytial Virus over Four
Japan’, Journal of Clinical Microbiology, 47: 2475–82. Consecutive Seasons in South Africa: Identification of New
Simmonds, P. et al. (2017) ‘Consensus Statement: Virus Subgroup A and B Genotypes’, Journal of General Virology, 82:
Taxonomy in the Age of Metagenomics’, Nature Reviews. 2117–24.
Microbiology, 15: 161–8. Vos, L. M. et al. (2019) ‘High Epidemic Burden of RSV Disease
Simoes, E. A. F. et al. (2015) ‘Challenges and Opportunities in Coinciding with Genetic Alterations Causing Amino Acid
Developing Respiratory Syncytial Virus Therapeutics’, Journal Substitutions in the RSV G-Protein during the 2016/2017
of Infectious Diseases, 211: S1–20. Season in the Netherlands’, Journal of Clinical Virology, 112: 20–6.
Stamatakis, A. (2014) ‘RAxML Version 8: A Tool for Phylogenetic WHO/OIE/FAO H5N1 Evolution Working Group. (2008) ‘Toward a
Analysis and Post-Analysis of Large Phylogenies’, Unified Nomenclature System for Highly Pathogenic Avian
Bioinformatics, 30: 1312–3. Influenza Virus (H5N1)’, Emerging Infectious Diseases, 14: e1.
Sullender, W. M. (2000) ‘Respiratory Syncytial Virus Genetic and Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis.
Antigenic Diversity’, Clinical Microbiology Reviews, 13: 1–15. New York: Springer-Verlag.
Tan, L. et al. (2012) ‘Genetic Variability among Complete Human Yu, G. et al. (2017) ‘Ggtree: An R Package for Visualization and
Respiratory Syncytial Virus Subgroup A Genomes: Bridging Annotation of Phylogenetic Trees with Their Covariates and
Molecular Evolutionary Dynamics and Epidemiology’, PLoS Other Associated Data’, Methods in Ecology and Evolution, 8:
One, 7: e51439. 28–36.
Thomson, E. et al. (2016) ‘Comparison of Next-Generation et al. (2018) ‘Two Methods for Mapping and Visualizing
Sequencing Technologies for Comprehensive Assessment of Associated Data on Phylogeny Using Ggtree’, Molecular Biology
Full-Length Hepatitis C Viral Genomes’, Journal of Clinical and Evolution, 35: 3041–3.
Microbiology, 54: 2470–84. Zlateva, K. T. et al. (2005) ‘Genetic Variability and Molecular
Trento, A. et al. (2015) ‘Conservation of G-Protein Epitopes in Evolution of the Human Respiratory Syncytial Virus Subgroup
Respiratory Syncytial Virus (Group A) Despite Broad Genetic B Attachment G Protein’, Journal of Virology, 79: 9157–67.
Diversity: Is Antibody Selection Involved in Virus Evolution?’ et al. (2004) ‘Molecular Evolution and Circulation Patterns
Journal of Virology, 89: 7776–85. of Human Respiratory Syncytial Virus Subgroup A: Positively
et al. (2003) ‘Major Changes in the G Protein of Human Selected Sites in the Attachment G Glycoprotein Molecular
Respiratory Syncytial Virus Isolates Introduced by a Evolution and Circulation Patterns of Human Respiratory
Duplication of 60 Nucleotides’, Journal of General Virology, 84: Syncytial Virus Subgroup A: Positi’, Journal of Virology, 78:
3115–20. 4675–83.
et al. (2006) ‘Natural History of Human Respiratory
Syncytial Virus Inferred from Phylogenetic Analysis of theYou can also read
